where the concrete formulation of objectives \(\mathcal {J}\)  can be derived from any existing DML method, e.g., contrastive loss [1]}, triplet loss [2]}, and MS loss [3]}. For example, if using triplet loss during training, we uniformly sample a mini-batch \(B\)  and follow our learning fashion as:
\(\mathcal {L}(B)=\sum \limits _{T \in \mathcal {T}_{i}} \sum _{\begin{array}{c}(a, p, n) \\ \sim {T} \circ B\end{array}}\left[ \left\Vert f_{a}-f_{p}\right\Vert _{2}-\left\Vert f_{a}-f_{n}\right\Vert _{2}+\alpha \right]_{+},\) 
